About the Role:As our Senior Machine Learning Engineer, you'll own the intelligence layer of Thalo's platform. We generate hundreds of gigabytes of HVAC sensor data no one in the world has seen before, and your job is to turn it into the detection algorithms, physics-based models, and product features that tell our customers exactly what's wrong with their equipment and what to do about it.
This is a hands-on, end-to-end role for someone who wants to own a problem from raw time-series data all the way to a shipped, customer-facing feature. You'll build and tune our issue-detection engine, put physics-based, ML, and LLM-powered models into production, establish how we evaluate and trust them, and work closely with our engineering, customer success, and business development teams to make sure the intelligence we ship is accurate, trustworthy, and genuinely useful in the field. You'll be a senior voice on a small, mighty team!
What we offer:- An immediate opportunity to make an impact fighting climate change with a mission-driven team.
- An in-person, collaborative culture. In our midtown Manhattan office, we not only have a stocked pantry but we also dedicate time to connect with each other during weekly happy hours and quarterly offsites.
- National subsidized healthcare plans for medical, dental, and vision insurance.
- Additional benefits include a 401(k) program, 12 weeks paid parental leave, and paid time off.
- Free mental health and professional coaching appointments through Lyra.
- At our ground-floor stage, our compensation structure places a strong emphasis on the value of high equity, with an annual compensation ranging from $150,000-$180,000.
What you'll do:- Own, extend, and improve Thalo's issue-detection engine spanning the electrical, refrigerant, and equipment-performance diagnostics at the core of our product
- Research, develop, and implement ML, statistical, and LLM-based models in production, working directly with first-of-its-kind streaming sensor time-series data
- Own our AI-evaluation practice: build labeled fault sets (from service outcomes, physics-vs-LLM disagreements, and field cross-checks), define accuracy metrics, and stand up an eval harness that regression-tests every prompt change, new detector, and model upgrade before it ships
- Turn model outputs into clear, actionable insights and reports our field, CS, and BD teams can confidently put in front of customers
- Continuously improve the data pipeline for large-scale ingestion, storage, transformation, and analysis so detection runs reliably and cost effectively as we scale
- Partner closely with hardware, software, and business teams to connect field and customer insights back into the product and document your work so the whole team can build on it
What you have:- 5+ years building and deploying ML or statistical models on production data, ideally in an early-stage startup environment
- M.S. or higher in a quantitative discipline such as math, physics, statistics, or data science (or equivalent applied experience)
- Strong applied experience with time-series or streaming sensor data, including anomaly detection, forecasting, signal processing, or similar
- Hands-on experience shipping production features on frontier LLMs (e.g., prompt engineering, structured output, tool-use/agents, and RAG) with the judgment to know when an LLM is the right tool versus a deterministic rule or a statistical model
- Experience evaluating AI systems: building eval sets, measuring precision/recall, using LLM-as-judge, and guarding against regressions as prompts and models change
- Fluency in Python and the modern data stack, with the software-engineering chops to ship production-grade code (not just notebooks)
- A real customer instinct: the ability to translate a model output into a plain-English insight a technician or building operator will trust and act on
- Curiosity about the physical world and the drive to understand the "why" behind the product, not just how to implement it
- A self-directed, ownership mindset and a habit of documenting and sharing context
Bonus points:- A passion for tackling climate change and promoting sustainability
- HVAC, refrigeration, combustion, building-systems, or energy-domain experience (a strong plus, but something we're happy to help the right person learn)
- Experience with agentic / tool-use systems, RAG over technical documentation, or LLM vision
- Familiarity with LLM cost/latency optimization (prompt caching, batch inference) and model governance (managing upgrades, monitoring output/score drift, A/B-testing context changes)
- Frontier-class LLM, open source LLM, and/or AWS Bedrock in production
- Full-stack comfort to take a feature to the UI (React/TypeScript); time-series databases (InfluxDB, TimescaleDB) and tools like Grafana; a degree in a quantitative or engineering discipline
$150,000 - $180,000 a year
